用外部推荐系统+LLM重排，提升对话推荐准确率
Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-based Reranking
- 用外部推荐系统生成候选物品，弥补LLM的领域知识不足
- 在ReDial和INSPIRED数据集上平均提升54%和25%准确率
- 适合需要高精度对话推荐的应用场景
我们针对大语言模型（LLMs）与外部推荐系统融合以增强对话推荐（CRS）领域专长的问题提出新方法。现有基于LLM的CRS主要依赖零样本/少样本生成推荐，但存在两大挑战：（1）缺乏领域适配时，LLM常推荐目标物品空间外的项目，导致准确率低；（2）过度依赖对话上下文进行内容推荐，忽视物品序列间的协同关系。为此，我们提出CARE框架：（a）将外部推荐系统作为领域专家，通过实体级洞察生成候选物品；（b）定制LLM作为重排器，利用上下文信息提升准确性。实验表明，引入CARE框架使LLM在ReDial和INSPIRED数据集上的推荐准确率分别平均提升54%和25%。最有效的策略是让LLM基于上下文洞察选择并重排外部推荐系统提供的候选物品。
原文摘要 · Abstract (English)
We tackle the challenge of integrating large language models (LLMs) with external recommender systems to enhance domain expertise in conversational recommendation (CRS). Current LLM-based CRS approaches primarily rely on zero/few-shot methods for generating item recommendations based on user queries, but this method faces two significant challenges: (1) without domain-specific adaptation, LLMs frequently recommend items not in the target item space, resulting in low recommendation accuracy; and (2) LLMs largely rely on dialogue context for content-based recommendations, neglecting the collaborative relationships among item sequences. To address these limitations, we introduce the CARE (Contextual Adaptation of Recommenders) framework. CARE (a) integrates external recommender systems as domain experts, producing candidate items through entity-level insights, and (b) customizes LLMs as rerankers to enhance the accuracy by leveraging contextual information. Our results demonstrate that incorporating CARE framework significantly enhances recommendation accuracy of LLMs by an average of 54% and 25% for ReDial and INSPIRED datasets. The most effective CARE strategy involves LLMs selecting and reranking candidate items that external recommenders provide based on contextual insights.
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